A Review on the Authenticity Verification of Deepfake Speech

doi:10.3969/j.issn.1671-1122.2026.03.003

Abstract

Abstract:

With the misuse of deepfake speech technology in telecom fraud and online disinformation dissemination, the authenticity verification of high-fidelity synthetic speech presents severe challenges for forensic practice. This paper focused on deepfake-oriented forensic speech authentication as the research subject, and established an integrated technical framework consisting of originality verification, integrity verification, and deepfake detection.For originality verification, this study examined the methodologies and applicable scopes of consistency checking for recording devices and system environments, as well as logical verification of file attributes and metadata. For integrity verification, it systematically elaborated the technical approaches of auditory examination, spectrographic analysis, and other signal-based forensic examinations. For deepfake detection, it summarized detection algorithms, benchmark datasets, and evaluation metrics from the perspectives of global discrimination and local tampering localization. The results demonstrate that an integrated technical paradigm combining file metadata analysis, traditional acoustic forensic examination, and deep learning detection is conducive to ensuring the interpretability, verifiability, and judicial admissibility of forensic identification, thereby providing a theoretical foundation and technical support for speech authenticity verification in complex network environments.

Key words: audio authenticity verification, deepfake speech detection, deep learning, sonogram examination

CLC Number:

TP309

XU Yanwei, TU Min, ZHANG Liang. A Review on the Authenticity Verification of Deepfake Speech[J]. Netinfo Security, 2026, 26(3): 367-377.

Figures/Tables 6

References 63

[1]	PHAM L, LAM P, TRAN D, et al. A Comprehensive Survey with Critical Analysis for Deepfake Speech Detection[EB/OL]. (2025-04-16)[2025-08-10]. https://www.skopik.at/ait/2025_csreview.pdf.
[2]	LI Menglu, AHMADIADLI Y, ZHANG Xiaoping. A Survey on Speech Deepfake Detection[J]. ACM Computing Surveys, 2025, 57(7): 1-38.
[3]	XIE Yuankun, CHENG Haonan, YE Long. A Survey on Deepfake Audio Detection[J]. Journal of Communication University of China (Natural Science Edition), 2024, 31(3): 26-33.
	谢元坤, 程皓楠, 叶龙. 深度伪造音频检测综述[J]. 中国传媒大学学报(自然科学版), 2024, 31(3):26-33.
[4]	XU Yuxiong, LI Bin, TAN Shunquan, et al. Research Progress on Speech Deepfake and Its Detection Techniques[J]. Journal of Image and Graphics, 2024, 29(8): 2236-2268. doi: 10.11834/jig.230476 URL
	许裕雄, 李斌, 谭舜泉, 等. 语音深度伪造及其检测技术研究进展[J]. 中国图象图形学报, 2024, 29(8):2236-2268.
[5]	LI Zeyu, ZHANG Xuhong, PU Yuwen, et al. A Survey of Multimodal Deepfake Generation and Detection Technologies[J]. Journal of Computer Research and Development, 2023, 60(6): 1396-1416.
	李泽宇, 张旭鸿, 蒲誉文, 等. 多模态深度伪造及检测技术综述[J]. 计算机研究与发展, 2023, 60(6):1396-1416.
[6]	SHEN J, PANG Ruoming, WEISS R J, et al. Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions[C]// IEEE. The 43rd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). New York: IEEE, 2018: 4779-4783.
[7]	WANG Yuxuan, SKERRY-RYAN R J, STANTON D, et al. Tacotron: A Fully End-to-End Text-to-Speech Synthesis Model[EB/OL]. (2017-03-29)[2025-08-10]. https://arxiv.org/abs/1703.10135.
[8]	REN Yi, RUAN Yang jun, TAN Xu, et al. FastSpeech: Fast, Robust and Controllable Text to Speech[J]. Advances in Neural Information Processing Systems, 2019, 32: 1-11.
[9]	POPOV V, VOVK I, GOGORYAN V, et al. Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech[EB/OL]. (2021-05-13)[2025-08-10]. https://arxiv.org/abs/2105.06337.
[10]	CHEN Sanyuan, WANG Chengyi, WU Yu, et al. Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers[J]. IEEE Transactions on Audio, Speech and Language Processing, 2025, 33: 705-718. doi: 10.1109/TASLPRO.2025.3530270 URL
[11]	GAO Junfeng, CHEN Junguo. Voice Conversion with Non-Parallel Corpus Based on Style-CycleGAN-VC[J]. Computer Applications and Software, 2021, 38(9): 133-139, 159.
	高俊峰, 陈俊国. 基于Style-CycleGAN-VC的非平行语料下的语音转换[J]. 计算机应用与软件, 2021, 38(9):133-139,159.
[12]	KAMEOKA H, KANEKO T, TANAKA K, et al. StarGAN-VC: Non-Parallel Many-to-Many Voice Conversion Using Star Generative Adversarial Networks[C]// IEEE. 2018 IEEE Spoken Language Technology Workshop (SLT). New York: IEEE, 2018: 266-273.
[13]	CHOI H Y, LEE S H, LEE S W. DDDM-VC: Decoupled Denoising Diffusion Models with Disentangled Representation and Prior Mixup for Verified Robust Voice Conversion[C]// AAAI. The AAAI Conference on Artificial Intelligence. Palo Alto: AAAI, 2024: 17862-17870.
[14]	KANEKO T, KAMEOKA H, TANAKA K, et al. Diff-VC: Diffusion-Based Non-Parallel Many-to-Many Voice Conversion[C]// IEEE. 2022 IEEE Spoken Language Technology Workshop (SLT). New York: IEEE, 2022: 730-737.
[15]	KIM J, KONG J, SON J. Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech[C]// IMLS. The 38th International Conference on Machine Learning. New York: PMLR, 2021: 5530-5540.
[16]	ZHANG Yuxiang, LI Zhuo, LU Jingze, et al. Spoof Speech Detection Based on Speaker Features[J]. Acta Acustica, 2025, 50(1): 201-210.
	张宇翔, 李茁, 陆镜泽, 等. 基于声纹特征的伪造语音检测[J]. 声学学报, 2025, 50(1):201-210.
[17]	BAEVSKI A, ZHOU Yuhao, MOHAMED A, et al. wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations[EB/OL]. (2020-12-06)[2025-07-10]. https://arxiv.org/abs/2006.11477.
[18]	GAMBIN A F, YAZIDI A, VASILAKOS A, et al. Deepfakes: Current and Future Trends[J]. Artificial Intelligence Review, 2024, 57-64.
[19]	ZENG Chunyan, WANG Zhifeng, WANG Jing, et al. Survey of Passive Detection of Digital Audio Tampering[J]. Computer Engineering and Applications, 2019, 55(2): 1-11, 99. doi: 10.3778/j.issn.1002-8331.1809-0324
	曾春艳, 王志锋, 王静, 等. 数字音频篡改被动检测研究综述[J]. 计算机工程与应用, 2019, 55(2):1-11,99. doi: 10.3778/j.issn.1002-8331.1809-0324
[20]	LIU Xuechen, WANG Xin, SAHIDULLAH M, et al. ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2023, 31: 2507-2522. doi: 10.1109/TASLP.2023.3285283 URL
[21]	WANG Xin, DELGADO H, SINGH V, et al. ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech[EB/OL]. (2025-02-13)[2025-08-10]. https://doi.org/10.1016/j.csl.2025.101825.
[22]	FRANK J, SCHONHERR L. WaveFake: A Dataset to Facilitate Audio Deepfake Detection[C]// NeurIPS. The 35th Conference on Neural Information Processing Systems Datasets and Benchmarks Track. Cambridge: MIT Press, 2021: 1-8.
[23]	YI Jiangyan, FU Ruibo, TAO Jianhua, et al. ADD 2022: The First Audio Deep Synthesis Detection Challenge[C]// IEEE. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). New York: IEEE, 2022: 9216-9220.
[24]	ZHANG Lin, WANG Xin, COOPER E, et al. The PartialSpoof Database and Countermeasures for the Detection of Short Fake Speech Segments Embedded in an Utterance[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2023, 31: 813-825. doi: 10.1109/TASLP.2022.3233236 URL
[25]	YI Jiangyan, BAI Ye, TAO Jianhua, et al. Half-Truth: A Partially Fake Audio Detection Dataset[C]// ISCA. International Speech Communication Association Annual Conference. New York: IEEE, 2021: 4321-4325.
[26]	ZHANG Bowen, CUI Hui, NGUYEN V, et al. Audio Deepfake Detection: What Has Been Achieved and What Lies Ahead[J]. Sensors, 2025, 25(7): 1989-2021. doi: 10.3390/s25071989 URL
[27]	KINNUNEN T, LEE K-A, DELGADO H, et al. T-DCF: A Detection Cost Function for the Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification[EB/OL]. (2018-04-25)[2025-08-10]. https://arxiv.org/abs/1804.09618.
[28]	LI Xiaolong, YU Nenghai, ZHANG Xinpeng, et al. Overview of Digital Media Forensics Technology[J]. Journal of Image and Graphics, 2021, 26(6): 1216-1226. doi: 10.11834/jig.210081 URL
	李晓龙, 俞能海, 张新鹏, 等. 数字媒体取证技术综述[J]. 中国图象图形学报, 2021, 26(6):1216-1226.
[29]	KOENIG B E, LACEY D S. Forensic Authenticity Analyses of the Header Data in Re-Encoded WMA Files from Small Olympus Audio Recorders[J]. Journal of the Audio Engineering Society, 2012, 60(4): 255-265.
[30]	LU Qimeng, SHI Shaopei, BIAN Xinwei, et al. A Method for Identifying Audio File Sources Based on Data Analysis[J]. Chinese Journal of Forensic Sciences, 2016(1): 37-44.
	卢启萌, 施少培, 卞新伟, 等. 一种基于数据分析的录音文件来源识别方法[J]. 中国司法鉴定, 2016(1):37-44.
[31]	ZENG Jinhua, XI Jianhua, SUN Weilong, et al. Authenticity Identification Technology of iPhone Recordings[J]. Chinese Journal of Forensic Sciences, 2020(5): 93-97. doi: 10.3969/j.issn.1671-2072.2020.05.015
	曾锦华, 奚建华, 孙维龙, 等. 苹果手机录制录音真实性鉴定技术[J]. 中国司法鉴定, 2020(5):93-97. doi: 10.3969/j.issn.1671-2072.2020.05.015
[32]	LIU Haoyang, GUO Hong. Research on Authenticity Appraisal of Instant Messaging Chat Records[J]. Chinese Journal of Forensic Sciences, 2025(4): 75-82. doi: 10.3969/j.issn.1671-2072.2025.04.008
	刘浩阳, 郭弘. 即时通讯聊天记录真实性鉴定研究[J]. 中国司法鉴定, 2025(4):75-82.
[33]	KIRCHHUEBEL C, BROWN G. Spoofed Speech from the Perspective of a Forensic Phonetician[C]// IEEE. 2022 Annual Conference of the International Speech Communication Association. New York: IEEE, 2022: 1308-1312.
[34]	ZHAO Hong, MALIK H. Audio Recording Location Identification Using Acoustic Environment Signature[J]. IEEE Transactions on Information Forensics and Security, 2013, 8(11): 1746-1759. doi: 10.1109/TIFS.2013.2278843 URL
[35]	HU Jiansong, YIN Qixin, CHEN Dingwen. A Systematic Forensic Study on the Authenticity (Integrity) of Voice Data[J]. Journal of Yunnan Police College, 2019(1): 56-70.
	胡健松, 殷启新, 陈丁文. 语音资料真实性(完整性)的系统鉴定研究[J]. 云南警官学院学报, 2019(1):56-70.
[36]	CHENG Hao, GUO Yangyang, WANG Tianyi, et al. Voice-Face Homogeneity Tells Deepfake[J]. ACM Transactions on Multimedia Computing, Communications, and Applications, 2024, 20(3): 1-21.
[37]	LIU Weifeng, SHE Tianyi, LIU Jiawei, et al. Lips Are Lying: Spotting the Temporal Inconsistency between Audio and Visual in Lip-Syncing DeepFakes[EB/OL]. (2024-01-28)[2025-08-10]. https://arxiv.org/abs/2401.15668.
[38]	ZAKARIAH M, KHAN M K, MALIK H. Digital Multimedia Audio Forensics: Past, Present and Future[J]. Multimedia Tools and Applications, 2018, 77: 1009-1040. doi: 10.1007/s11042-016-4277-2 URL
[39]	OZGEN E, ALTAY S Y. Detecting Audio Copy-Move Forgeries on Mel Spectrograms via Hybrid Keypoint Features[EB/OL]. [2025-08-10]. https://www.mdpi.com/2076-3417/15/21/11845.
[40]	NI Lingge, WANG Huapeng, LIU Yuanzhou, et al. The Research on the Impact of Replay Attack on Speech Authenticity Verification[J]. Journal of People’s Public Security University of China (Natural Science Edition), 2020, 26(4): 8-14.
	倪令格, 王华朋, 刘元周, 等. 翻录对语音真实性检验的影响研究[J]. 中国人民公安大学学报(自然科学版), 2020, 26(4):8-14.
[41]	RAN Qibin, HUANG Wei. Acoustic Comparison of Voice Quality between Synthetic and Natural Speech: Taking 18 Languages as an Example[J]. Journal of Tianjin Foreign Studies University, 2024, 31(5): 73-87, 112-113.
	冉启斌, 黄玮. 合成语音与自然语音嗓音的声学对比分析——以18种语言为例[J]. 天津外国语大学学报, 2024, 31(5):73-87,112-113.
[42]	ZHOU Xinyu, ZHANG Yujin, WANG Yongqi, et al. Pyramid Feature Attention Network for Speech Resampling Detection[EB/OL]. (2024-06-01)[2025-08-10]. https://www.mdpi.com/2076-3417/14/11/4803.
[43]	JIN Chao, WANG Rangding, YAN Diqun, et al. An Efficient Algorithm for Double Compressed AAC Audio Detection[J]. Multimedia Tools and Applications, 2016, 75(8): 1-18. doi: 10.1007/s11042-014-2221-x URL
[44]	GRIGORAS C. Digital Audio Recording Analysis: The Electric Network Frequency Criterion[J]. International Journal of Speech Language and the Law, 2005, 12(1): 63-76. doi: 10.1558/sll.2005.12.1.63 URL
[45]	CHUANG Weihong, GARG R, WU Min. Anti-Forensics and Countermeasures of Electrical Network Frequency Analysis[J]. IEEE Transactions on Information Forensics and Security, 2013, 8(12): 2073-2088. doi: 10.1109/TIFS.2013.2285515 URL
[46]	LIN Xiaodan, KANG Xiangui. Supervised Audio Tampering Detection Using an Autoregressive Model[C]// IEEE. IEEE International Conference on Acoustics, Speech and Signal Processing. New York: IEEE, 2017: 2142-2146.
[47]	WU Zhizheng, KINNUNEN T, EVANS N, et al. ASVspoof 2015: The First Automatic Speaker Verification Spoofing and Countermeasures Challenge[C]// ISCA. The 16th Annual Conference of the International Speech Communication Association. New York: IEEE, 2015: 2037-2041.
[48]	TODISCO M, DELGADO H, EVANS N. Constant Q Cepstral Coefficients: A Spoofing Countermeasure for Automatic Speaker Verification[J]. Computer Speech & Language, 2017, 45: 516-535.
[49]	XUE Jun, FAN Cunhang, LYU Zhao, et al. Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features[C]// ACM. The 1st International Workshop on Deepfake Detection for Audio Multimedia. New York: ACM, 2022: 19-26.
[50]	TAHAOGLU G, BARACCHI D, SHULLANI D, et al. Deepfake Audio Detection with Spectral Features and ResNeXt-Based Architecture[EB/OL]. (2025-07-01)[2025-08-10]. https://flore.unifi.it/retrieve/b86b60b7-d663-4a1e-b421-ede9dd11d1eb/1-s2.0-S0950705125007725-main.pdf.
[51]	DESPLANQUES B, THIENPONDT J, DEMUYNCK K, et al. ECAPA-TDNN:Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification[C]// ISCA. Interspeech 2020. New York: IEEE, 2020: 3830-3834.
[52]	JUNG J, KIM Y, HEO H, et al. Pushing the Limits of Raw Waveform Speaker Recognition[C]// ISCA. The 23rd International Conference on Speech Communication (Interspeech 2022). New York: IEEE, 2022: 2228-2232.
[53]	LU Jingze, ZHANG Yuxiang, SHANG Zengqiang, et al. Spoofed Speech Detection Based on Glottal Flow and Voiceprint Features[J]. Acta Acustica, 2025, 50(6): 1679-1689.
	陆镜泽, 张宇翔, 尚增强, 等. 结合声门流和声纹特征的伪造语音检测[J]. 声学学报, 2025, 50(6):1679-1689.
[54]	LIU Xiaolong, YU Yang, LI Xiaolong, et al. Magnifying Multimodal Forgery Clues for Deepfake Detection[EB/OL]. (2023-07-07)[2025-08-10]. https://doi.org/10.1016/j.image.2023.117010.
[55]	TAK H, TODISCO M, WANG Xin, et al. Automatic Speaker Verification Spoofing and Deepfake Detection Using Wav2vec 2.0 and Data Augmentation[C]// ISCA. Odyssey 2022:The Speaker and Language Recognition Workshop. New York: IEEE, 2022: 100-106.
[56]	LAVRENTYEVA G, NOVOSELOV S, TSEREN A, et al. STC Anti-Spoofing Systems for the ASVspoof 2019 Challenge[C]// ISCA. The 20th Annual Conference of the International Speech Communication Association. New York: IEEE, 2019: 1033-1037.
[57]	TAK H, PATINO J, TODISCO M, et al. End-to-End Anti-Spoofing with RawNet2[C]// IEEE. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). New York: IEEE, 2021: 6369-6373.
[58]	JUNG J W, HEO H S, TAK H, et al. AASIST: Audio Anti-Spoofing Using Integrated Spectro-Temporal Graph Attention Networks[C]// IEEE. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). New York: IEEE, 2022: 6367-6371.
[59]	LIU Xiaohui, CHEN Xiaopeng, GAO Tianxiang, et al. Leveraging Positional-Related Local-Global Dependency for Synthetic Speech Detection[C]// IEEE. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). New York: IEEE, 2023: 1-5.
[60]	CAI Zexin, LI Ming. Integrating Frame-Level Boundary Detection and Deepfake Detection for Locating Manipulated Regions in Partially Spoofed Audio Forgery Attacks[J]. Computer Speech & Language, 2024, 85: 97-106.
[61]	LI Menglu, ZHANG Xiaoping, ZHAO Lian. Frame-Level Temporal Difference Learning for Partial Deepfake Speech Detection[J]. IEEE Signal Processing Letters, 2025, 32: 3052-3056. doi: 10.1109/LSP.2025.3592111 URL
[62]	YANG Tianle, SUN Chengzhe, LYU Siwei, et al. Forensic Deepfake Audio Detection Using Segmental Speech Features[EB/OL]. (2025-05-20)[2025-08-10]. https://doi.org/10.1016/j.forsciint.2025.112768.
[63]	ZHANG Kuiyuan, HUA Zhongyun, LAN Rushi, et al. Phoneme-Level Feature Discrepancies: A Key to Detecting Sophisticated Speech Deepfakes[C]// AAAI. AAAI Conference on Artificial Intelligence. Palo Alto: AAAI, 2025: 1066-1074.

生成路线	代表模型	声学特征	典型伪造痕迹特征
TTS- 自回归	WaveNet^[6]、 Tacotron系列^[7]	原始波形/ Mel频谱	相位连续性异常、基频平滑异常
TTS- 非自回归	FastSpeech 系列^[8]	音素时长、F0	时长量化边界、频谱包络过平滑、韵律模式单一
TTS- 端到端/ 大模型	VALL-E^[10]、 VITS^[15]	时域波形、神经音频编码	韵律不连贯、噪声底纹不稳定、上下文切换伪影
TTS- 扩散模型	Grad-TTS^[9]	扩散轨迹、 Mel频谱	频带能量分布异常、去噪残留伪影
VC-非平行	CycleGAN-VC^[11]、 StarGAN-VC^[12]	Mel频谱	说话人特征混叠、共振峰偏移、说话人特征残留
VC-扩散模型	DDDM-VC^[13]、 Diff-VC^[14]	Mel频谱、扩散轨迹	频带能量衰减不均、共振峰轨迹异常、跨帧相关性异常

数据集	伪造类型	语言	常用评估指标
ASVspoof 系列^[20,21]	TTS、VC（LA）、重放（PA）、深度伪造（DF）	英语	EER、min t-DCF
WaveFake^[22]	TTS	英语/日语	EER、AUC
ADD LF^[23]、 CFAD	TTS、VC	中文	EER
ITW	TTS	英语	EER
PartialSpoof^[24]	帧级部分伪造	英语	EER
ADD PF^[23]	局部伪造	中文	EER、F1
HAD^[25]	局部拼接/篡改	中文	EER、F1

鉴定维度	检验思路	检验内容	关键技术方法
文件层	文件结构与元数据一致性	哈希值校验、容器结构一致性、元数据逻辑核验、系统底层日志分析	设备指纹比对、多源系统交叉核验^[29-32]
听觉感知与语义层	语音听觉感知特征	语义逻辑、语音自然性	听觉感知分析^[33-35]
听觉感知与语义层	多模态检材一致性	音视频时序同步	跨模态特征一致性关联^[36,37]
声学特征层	声学统计一致性	频谱连续性、噪声平稳性、谐噪统计一致性	波形分析、长时平均功率谱（LTAS）、谐噪比统计与拼接痕迹检测^[38-41]
编码与物理信号层	编码痕迹与环境信号溯源	频谱与噪声稳定性、编码痕迹（MDCT）、ENF轨迹	重采样、重压缩、电网频率信号（ENF）分析^[42-46]
深度伪造语音检测	全局伪造检测	前端特征提取	传统手工特征（MFCC、LFCC、CQCC）、声源生理特征、F0与复频谱表征、自监督语音表征^[47-55]
	全局伪造检测	后端主干网络	LCNN+最大特征图激活、RawNet2、AASIST+ 图注意力机制^[56-59]
	局部伪造检测	帧级/音素级边界、时序建模、局部频谱伪影	片段级伪造定位、帧级定位、音素级一致性验证^[60-63]